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Poster
Learning person-object interactions for action recognition in still images
Vincent Delaitre · Josef Sivic · Ivan Laptev

Wed Dec 14 08:45 AM -- 02:59 PM (PST) @ None #None
in Poster Session »

We investigate a discriminatively trained model of person-object interactions for recognizing common human actions in still images. We build on the locally order-less spatial pyramid bag-of-features model, which was shown to perform extremely well on a range of object, scene and human action recognition tasks. We introduce three principal contributions. First, we replace the standard quantized local HOG/SIFT features with stronger discriminatively trained body part and object detectors. Second, we introduce new person-object interaction features based on spatial co-occurrences of individual body parts and objects. Third, we address the combinatorial problem of a large number of possible interaction pairs and propose a discriminative selection procedure using a linear support vector machine (SVM) with a sparsity inducing regularizer. Learning of action-specific body part and object interactions bypasses the difficult problem of estimating the complete human body pose configuration. Benefits of the proposed model are shown on human action recognition in consumer photographs, outperforming the strong bag-of-features baseline.

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Author Information

Vincent Delaitre (Ecole Normale Supérieure)
Josef Sivic (Inria and Czech Technical University)
Ivan Laptev (INRIA)

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